Mitigating over-fitting and improving generalization on test data are central goals in machine learning.
One approach to accomplish this is regularization, which can be either data-agnostic or data-dependent (e.g., explicitly requiring the use of domain knowledge).
Noise injection is a typical example of data-agnostic regularization [1]}, where noise can be injected into the input data [2]}, or the activation functions [3]}, or the hidden layers of neural networks [4]}, [5]}.
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